scholarly journals Face Recognition Systems: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 342 ◽  
Author(s):  
Yassin Kortli ◽  
Maher Jridi ◽  
Ayman Al Falou ◽  
Mohamed Atri
Keyword(s):  
Face Recognition ◽  
Autonomous Vehicles ◽  
Detailed Comparison ◽  
Image Features ◽  
Face Image ◽  
Image Description ◽  
Future Directions ◽  
The Past ◽  
Hybrid Approaches ◽  
Recognition Systems

Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

scholarly journals Face Recognition Based on Histogram Equalization and LBP Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 33
Author(s):  
Herman Kh. Omar ◽  
Nada E. Tawfiq
Keyword(s):  
Face Recognition ◽  
Texture Classification ◽  
Histogram Equalization ◽  
Recognition System ◽  
Input Image ◽  
Image Features ◽  
Face Image ◽  
Wide Field ◽  
Face Recognition System ◽  
The Face

In the recent time bioinformatics take wide field in image processing. Face recognition which is basically the task of recognizing a person based on its facial image. It has become very popular in the last two decades, mainly because of the new methods developed and the high quality of the current visual instruments. There are different types of face recognition algorithms, and each method has a different approach to extract the image features and perform the matching with the input image. In this paper the Local Binary Patterns (LBP) was used, which is a particular case of the Texture Spectrum model, and powerful feature for texture classification. The face recognition system consists of recognizing the faces acquisition from a given data base via two phases. The most useful and unique features of the face image are extracted in the feature extraction phase. In the classification the face image is compared with the images from the database. The proposed algorithm for face recognition in this paper adopt the LBP features encode local texture information with default values. Apply histogram equalization and Resize the image into 80x60, divide it to five blocks, then Save every LBP feature as a vector table. Matlab R2019a was used to build the face recognition system. The Results which obtained are accurate and they are 98.8% overall (500 face image).

Face in Person Re-Identification

2014 ◽  
pp. 273-288
Author(s):  
Andrea F. Abate ◽  
Stefano Ricciardi ◽  
Genoveffa Tortora
Keyword(s):  
Face Recognition ◽  
Response Speed ◽  
Identity Verification ◽  
Hybrid Approaches ◽  
The Face ◽  
Recognition Systems ◽  
New Research ◽  
New Occurrences ◽  
Large Corpus

The face represents one of the most diffused and established biometrics for both identity verification and recognition with a large corpus of research focused on advancing the accuracy, the robustness, and the response speed of face recognition systems by means of 2D, 3D, and hybrid approaches. One of the new research lines emerging in this field during the last years is face-based people re-identification, namely the task of recognizing new occurrences of an individual's face once it has been detected and initialized at a given time on the same location or eventually at other locations covered by a network of non-overlapping cameras. In this chapter, the main issues and challenges specifically related to face-based people re-identification are described, and the most promising techniques and results proposed on this topic so far are presented and discussed.

scholarly journals A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shoujun Tang ◽  
Mohammad Shabaz
Keyword(s):  
Face Recognition ◽  
Basal Ganglia ◽  
Recognition Rate ◽  
Great Accuracy ◽  
Recognition Algorithm ◽  
Image Features ◽  
Face Image ◽  
Geometric Feature ◽  
Face Images ◽  
High Recognition Accuracy

Face recognition is one of the popular areas of research in the field of computer vision. It is mainly used for identification and security system. One of the major challenges in face recognition is identification under numerous illumination environments by changing the direction of light or modifying the lighting magnitude. Exacting illumination invariant features is an effective approach to solve this problem. Conventional face recognition algorithms based on nonsubsampled contourlet transform (NSCT) and bionic mode are not capable enough to recognize the similar faces with great accuracy. Hence, in this paper, an attempt is made to propose an enhanced cerebellum-basal ganglia mechanism (CBGM) for face recognition. The integral projection and geometric feature assortment method are used to acquire the facial image features. The cognition model is deployed which is based on the cerebellum-basal ganglia mechanism and is applied for extraction of features from the face image to achieve greater accuracy for recognition of face images. The experimental results reveal that the enhanced CBGM algorithm can effectively recognize face images with greater accuracy. The recognition rate of 100 AR face images has been found to be 96.9%. The high recognition accuracy rate has been achieved by the proposed CBGM technique.

A Face Recognition Algorithm Based on Intermediate Layers Connected by the CNN

2021 ◽  
Author(s):  
Yuxiang Long
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Network Model ◽  
Recognition Algorithm ◽  
Image Features ◽  
Face Image ◽  
Face Features ◽  
Intermediate Layers ◽  
Training Samples ◽  
The Face

Face recognition is difficult due to the higher dimension of face image features and fewer training samples. Firstly, in order to improve the performance of feature extraction, we inventively construct a double hierarchical network structure convolution neural network (CNN) model. The front-end network adopts a relatively simple network model to achieve rough feature extraction from input images and obtain multiple suspect face candidate windows. The back-end network uses a relatively complex network model to filter the best detection window and return the face size and position by nonmaximum suppression. Then, in order to fully extract the face features in the optimal window, a face recognition algorithm based on intermediate layers connected by the deep CNN is proposed in this paper. Based on AlexNet, the front, intermediate and end convolution layers are combined by deep connection. Then, the feature vector describing the face image is obtained by the operation of the pooling layer and the full connection layer. Finally, the auxiliary classifier training method is used to train the model to ensure the effectiveness of the features of the intermediate layer. Experimental results based on open face database show that the recognition accuracy of the proposed algorithm is higher than that of other face recognition algorithms compared in this paper.

scholarly journals RTOB SLAM: Real-Time Onboard Laser-Based Localization and Mapping

Vehicles ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 778-789
Author(s):  
Leonard Bauersfeld ◽  
Guillaume Ducard
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Laser Scanner ◽  
Dynamic Environment ◽  
Detailed Comparison ◽  
Scan Matching ◽  
The Past ◽  
Current State ◽  
Localization And Mapping ◽  
Small Form Factor

RTOB-SLAM is a new low-computation framework for real-time onboard simultaneous localization and mapping (SLAM) and obstacle avoidance for autonomous vehicles. A low-resolution 2D laser scanner is used and a small form-factor computer perform all computations onboard. The SLAM process is based on laser scan matching with the iterative closest point technique to estimate the vehicle’s current position by aligning the new scan with the map. This paper describes a new method which uses only a small subsample of the global map for scan matching, which improves the performance and allows for a map to adapt to a dynamic environment by partly forgetting the past. A detailed comparison between this method and current state-of-the-art SLAM frameworks is given, together with a methodology to choose the parameters of the RTOB-SLAM. The RTOB-SLAM has been implemented in ROS and perform well in various simulations and real experiments.

Sketch Face Recognition: P-HOG Multi-Features Fusion

2019 ◽  
Vol 33 (04) ◽  
pp. 1956003
Author(s):  
Zhenxue Chen ◽  
Saisai Yao ◽  
Chengyun Liu ◽  
Lei Cai
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Image Features ◽  
Face Image ◽  
Biometric Recognition ◽  
Features Fusion ◽  
Weighted Fusion ◽  
Criminal Suspect ◽  
The Face ◽  
Global And Local

With the development of biometric recognition technology, sketch face recognition has been widely applied to assist the police to confirm the identity of the criminal suspect. Most of the present recognition methods use the image features directly, in which the key parts can’t be used sufficiently. This paper presents a sketch face recognition method based on P-HOG multi-features weighted fusion. Firstly, the global face image and the local face image which contains key components of the face are divided into patches based on spatial scale pyramid, and then the global P-HOG features and local P-HOG features are extracted, respectively. After that, the dimensions of global and local features are reduced using PCA and NLDA. Finally, the features are weighted based on sensitivity and fused. The nearest neighbor classifier is used to complete the final recognition. The experimental results on different databases show that the proposed method outperforms state-of-the-art methods.

On Future Directions in Pathology

1982 ◽  
Vol 40 ◽  
pp. 274-277
Author(s):  
Benjamin F. Trump ◽  
Irene K. Berezesky ◽  
Raymond T. Jones
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Clinical Pathology ◽  
Future Directions ◽  
Diagnostic Pathology ◽  
The Past ◽  
Transmission Electron ◽  
Organ Systems ◽  
The Many

The role of electron microscopy and associated techniques is assured in diagnostic pathology. At the present time, most of the progress has been made on tissues examined by transmission electron microscopy (TEM) and correlated with light microscopy (LM) and by cytochemistry using both plastic and paraffin-embedded materials. As mentioned elsewhere in this symposium, this has revolutionized many fields of pathology including diagnostic, anatomic and clinical pathology. It began with the kidney; however, it has now been extended to most other organ systems and to tumor diagnosis in general. The results of the past few years tend to indicate the future directions and needs of this expanding field. Now, in addition to routine EM, pathologists have access to the many newly developed methods and instruments mentioned below which should aid considerably not only in diagnostic pathology but in investigative pathology as well.

Autonomous vehicles and freight traffic: towards better efficiency of road, rail or urban logistics?

2018 ◽  
Vol 58 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Péter Bucsky
Keyword(s):  
Autonomous Vehicles ◽  
Road Transport ◽  
Transport Sector ◽  
Future Directions ◽  
Car Industry ◽  
Level Of Automation ◽  
Current State ◽  
Freight Traffic ◽  
Levels Of Automation ◽  
Private Car

Abstract The freight transport sector is a low profit and high competition business and therefore has less ability to invest in research and development in the field of autonomous vehicles (AV) than the private car industry. There are already different levels of automation technologies in the transport industry, but most of these are serving niche demands and answers have yet to be found about whether it would be worthwhile to industrialise these technologies. New innovations from different fields are constantly changing the freight traffic industry but these are less disruptive than on other markets. The aim of this article is to show the current state of development of freight traffic with regards to AVs and analyse which future directions of development might be viable. The level of automation is very different in the case of different transport modes and most probably the technology will favour road transport over other, less environmentally harmful traffic modes.

Comparison and Analysis of Computational Methods for Identifying N6 - methyladenosine Sites in Saccharomyces Cerevisiae

2020 ◽  
Vol 26 ◽  
Author(s):  
Pengmian Feng ◽  
Lijing Feng ◽  
Chaohui Tang
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Computational Methods ◽  
Computational Analysis ◽  
Computational Prediction ◽  
Experimental Methods ◽  
Biological Processes ◽  
Biological Functions ◽  
Precise Location ◽  
Future Directions ◽  
The Past

Background and Purpose: N 6 -methyladenosine (m6A) plays critical roles in a broad set of biological processes. Knowledge about the precise location of m6A site in the transcriptome is vital for deciphering its biological functions. Although experimental techniques have made substantial contributions to identify m6A, they are still labor intensive and time consuming. As good complements to experimental methods, in the past few years, a series of computational approaches have been proposed to identify m6A sites. Methods: In order to facilitate researchers to select appropriate methods for identifying m6A sites, it is necessary to give a comprehensive review and comparison on existing methods. Results: Since researches on m6A in Saccharomyces cerevisiae are relatively clear, in this review, we summarized recent progresses on computational prediction of m6A sites in S. cerevisiae and assessed the performance of existing computational methods. Finally, future directions of computationally identifying m6A sites were presented. Conclusion: Taken together, we anticipate that this review will provide important guides for computational analysis of m 6A modifications.

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